Methods for Decision Making with Insufficient Knowledge in Agriculture

Systems studied and implemented in biological and agricultural domains are characterized by manifestations of various forms of uncertainty. These include insufficient or incomplete knowledge, vague information and approximate and intuitive methods for problem solving. In these cases, any modeling and automation of a decision making process should provide mechanisms for representing and managing uncertain knowledge. In this paper, we present a conceptual framework for incorporating uncertainty modeling into Decision Support Systems applied to agricultural problems space. Such systems represent a more realistic approach, taking into account the available information, without enforcing an artificial accuracy. Additionally, through their application to real world problems, they provide a better insight into the algorithms and mathematical models used.